Literature DB >> 23387755

The effect of averaging adjacent planes for artifact reduction in matrix inversion tomosynthesis.

Devon J Godfrey1, H Page McAdams, James T Dobbins.   

Abstract

PURPOSE: Matrix inversion tomosynthesis (MITS) uses linear systems theory and knowledge of the imaging geometry to remove tomographic blur that is present in conventional backprojection tomosynthesis reconstructions, leaving in-plane detail rendered clearly. The use of partial-pixel interpolation during the backprojection process introduces imprecision in the MITS modeling of tomographic blur, and creates low-contrast artifacts in some MITS planes. This paper examines the use of MITS slabs, created by averaging several adjacent MITS planes, as a method for suppressing partial-pixel artifacts.
METHODS: Human chest tomosynthesis projection data, acquired as part of an IRB-approved pilot study, were used to generate MITS planes, three-plane MITS slabs (MITSa3), five-plane MITS slabs (MITSa5), and seven-plane MITS slabs (MITSa7). These were qualitatively examined for partial-pixel artifacts and the visibility of normal and abnormal anatomy. Additionally, small (5 mm) subtle pulmonary nodules were simulated and digitally superimposed upon human chest tomosynthesis projection images, and their visibility was qualitatively assessed in the different reconstruction techniques. Simulated images of a thin wire were used to generate modulation transfer function (MTF) and slice-sensitivity profile curves for the different MITS and MITS slab techniques, and these were examined for indications of partial-pixel artifacts and frequency response uniformity. Finally, mean-subtracted, exposure-normalized noise power spectra (ENNPS) estimates were computed and compared for MITS and MITS slab reconstructions, generated from 10 sets of tomosynthesis projection data of an acrylic slab. The simulated in-plane MTF response of each technique was also combined with the square root of the ENNPS estimate to yield stochastic signal-to-noise ratio (SNR) information about the different reconstruction techniques.
RESULTS: For scan angles of 20° and 5 mm plane separation, seven MITS planes must be averaged to sufficiently remove partial-pixel artifacts. MITSa7 does appear to subtly reduce the contrast of high-frequency "edge" information, but the removal of partial-pixel artifacts makes the appearance of low-contrast, fine-detail anatomy even more conspicuous in MITSa7 slices. MITSa7 also appears to render simulated subtle 5 mm pulmonary nodules with greater visibility than MITS alone, in both the open lung and regions overlying the mediastinum. Finally, the MITSa7 technique reduces stochastic image variance, though the in-plane stochastic SNR (for very thin objects which do not span multiple MITS planes) is only improved at spatial frequencies between 0.05 and 0.20 cycles∕mm.
CONCLUSIONS: The MITSa7 method is an improvement over traditional single-plane MITS for thoracic imaging and the pulmonary nodule detection task, and thus the authors plan to use the MITSa7 approach for all future MITS research at the authors' institution.

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Mesh:

Year:  2013        PMID: 23387755      PMCID: PMC3562331          DOI: 10.1118/1.4773891

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  17 in total

1.  Detection of subtle lung nodules: relative influence of quantum and anatomic noise on chest radiographs.

Authors:  E Samei; M J Flynn; W R Eyler
Journal:  Radiology       Date:  1999-12       Impact factor: 11.105

2.  Digital tomosynthesis of the chest: utility for detection of lung metastasis in patients with colorectal cancer.

Authors:  H N Jung; M J Chung; J H Koo; H C Kim; K S Lee
Journal:  Clin Radiol       Date:  2011-09-21       Impact factor: 2.350

3.  Subtle lung nodules: influence of local anatomic variations on detection.

Authors:  Ehsan Samei; Michael J Flynn; Edward Peterson; William R Eyler
Journal:  Radiology       Date:  2003-05-15       Impact factor: 11.105

4.  Optimization of the matrix inversion tomosynthesis (MITS) impulse response and modulation transfer function characteristics for chest imaging.

Authors:  Devon J Godfrey; H P McAdams; James T Dobbins
Journal:  Med Phys       Date:  2006-03       Impact factor: 4.071

5.  Digital tomosynthesis of the chest for lung nodule detection: interim sensitivity results from an ongoing NIH-sponsored trial.

Authors:  T Dobbins James; H Page McAdams; Jae-Woo Song; Christina M Li; Devon J Godfrey; David M DeLong; Sang-Hyun Paik; Santiago Martinez-Jimenez
Journal:  Med Phys       Date:  2008-06       Impact factor: 4.071

6.  Stochastic noise characteristics in matrix inversion tomosynthesis (MITS).

Authors:  Devon J Godfrey; H P McAdams; James T Third Dobbins
Journal:  Med Phys       Date:  2009-05       Impact factor: 4.071

7.  Simulation of subtle lung nodules in projection chest radiography.

Authors:  E Samei; M J Flynn; W R Eyler
Journal:  Radiology       Date:  1997-01       Impact factor: 11.105

8.  DQE(f) of four generations of computed radiography acquisition devices.

Authors:  J T Dobbins; D L Ergun; L Rutz; D A Hinshaw; H Blume; D C Clark
Journal:  Med Phys       Date:  1995-10       Impact factor: 4.071

Review 9.  Digital tomosynthesis of the chest.

Authors:  James T Dobbins; H Page McAdams; Devon J Godfrey; Christina M Li
Journal:  J Thorac Imaging       Date:  2008-05       Impact factor: 3.000

10.  Comparison of chest tomosynthesis and chest radiography for detection of pulmonary nodules: human observer study of clinical cases.

Authors:  Jenny Vikgren; Sara Zachrisson; Angelica Svalkvist; Ase A Johnsson; Marianne Boijsen; Agneta Flinck; Susanne Kheddache; Magnus Båth
Journal:  Radiology       Date:  2008-10-10       Impact factor: 11.105

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Journal:  Phys Med Biol       Date:  2014-04-17       Impact factor: 3.609

2.  A prototype Multi-X-ray-source array (MXA) for digital breast tomosynthesis.

Authors:  Amy E Becker; Andrew M Hernandez; John M Boone; Paul R Schwoebel
Journal:  Phys Med Biol       Date:  2020-12-18       Impact factor: 3.609

3.  A Novel Recurrent Neural Network-Based Ultra-Fast, Robust, and Scalable Solver for Inverting a "Time-Varying Matrix".

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Journal:  Sensors (Basel)       Date:  2019-09-16       Impact factor: 3.576

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